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Module « numpy.matlib »

Fonction nanstd - module numpy.matlib

Signature de la fonction nanstd

def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>, *, where=<no value>, mean=<no value>, correction=<no value>) 

Description

help(numpy.matlib.nanstd)

Compute the standard deviation along the specified axis, while
ignoring NaNs.

Returns the standard deviation, a measure of the spread of a
distribution, of the non-NaN array elements. The standard deviation is
computed for the flattened array by default, otherwise over the
specified axis.

For all-NaN slices or slices with zero degrees of freedom, NaN is
returned and a `RuntimeWarning` is raised.

Parameters
----------
a : array_like
    Calculate the standard deviation of the non-NaN values.
axis : {int, tuple of int, None}, optional
    Axis or axes along which the standard deviation is computed. The default is
    to compute the standard deviation of the flattened array.
dtype : dtype, optional
    Type to use in computing the standard deviation. For arrays of
    integer type the default is float64, for arrays of float types it
    is the same as the array type.
out : ndarray, optional
    Alternative output array in which to place the result. It must have
    the same shape as the expected output but the type (of the
    calculated values) will be cast if necessary.
ddof : {int, float}, optional
    Means Delta Degrees of Freedom.  The divisor used in calculations
    is ``N - ddof``, where ``N`` represents the number of non-NaN
    elements.  By default `ddof` is zero.

keepdims : bool, optional
    If this is set to True, the axes which are reduced are left
    in the result as dimensions with size one. With this option,
    the result will broadcast correctly against the original `a`.

    If this value is anything but the default it is passed through
    as-is to the relevant functions of the sub-classes.  If these
    functions do not have a `keepdims` kwarg, a RuntimeError will
    be raised.
where : array_like of bool, optional
    Elements to include in the standard deviation.
    See `~numpy.ufunc.reduce` for details.

    .. versionadded:: 1.22.0

mean : array_like, optional
    Provide the mean to prevent its recalculation. The mean should have
    a shape as if it was calculated with ``keepdims=True``.
    The axis for the calculation of the mean should be the same as used in
    the call to this std function.

    .. versionadded:: 2.0.0

correction : {int, float}, optional
    Array API compatible name for the ``ddof`` parameter. Only one of them
    can be provided at the same time.

    .. versionadded:: 2.0.0

Returns
-------
standard_deviation : ndarray, see dtype parameter above.
    If `out` is None, return a new array containing the standard
    deviation, otherwise return a reference to the output array. If
    ddof is >= the number of non-NaN elements in a slice or the slice
    contains only NaNs, then the result for that slice is NaN.

See Also
--------
var, mean, std
nanvar, nanmean
:ref:`ufuncs-output-type`

Notes
-----
The standard deviation is the square root of the average of the squared
deviations from the mean: ``std = sqrt(mean(abs(x - x.mean())**2))``.

The average squared deviation is normally calculated as
``x.sum() / N``, where ``N = len(x)``.  If, however, `ddof` is
specified, the divisor ``N - ddof`` is used instead. In standard
statistical practice, ``ddof=1`` provides an unbiased estimator of the
variance of the infinite population. ``ddof=0`` provides a maximum
likelihood estimate of the variance for normally distributed variables.
The standard deviation computed in this function is the square root of
the estimated variance, so even with ``ddof=1``, it will not be an
unbiased estimate of the standard deviation per se.

Note that, for complex numbers, `std` takes the absolute value before
squaring, so that the result is always real and nonnegative.

For floating-point input, the *std* is computed using the same
precision the input has. Depending on the input data, this can cause
the results to be inaccurate, especially for float32 (see example
below).  Specifying a higher-accuracy accumulator using the `dtype`
keyword can alleviate this issue.

Examples
--------
>>> import numpy as np
>>> a = np.array([[1, np.nan], [3, 4]])
>>> np.nanstd(a)
1.247219128924647
>>> np.nanstd(a, axis=0)
array([1., 0.])
>>> np.nanstd(a, axis=1)
array([0.,  0.5]) # may vary

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